基于距离偏差的模型定向加性优化 可分解问题

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摘要翻译：
对于许多优化问题，可以定义一个问题变量之间的距离度量，该度量与变量之间相互作用的可能性和强度相关。例如，可以定义一个度量，使得相对于该度量彼此更接近的变量之间的依赖性预期强于距离更远的变量之间的依赖性。本文的目的是描述一种方法，将这样一个特定问题的距离度量与从以前的分布估计算法运行中获得的概率模型中挖掘的信息相结合，以提高速度、精度和可靠性来解决未来类似类型的问题实例。虽然本文的重点是可加分解问题和层次贝叶斯优化算法，但将该方法推广到其他模型导向优化技术和其他问题类别应该是简单的。与以往提出的经验学习方法相比，该方法具有更强的实用性和更广泛的适用性。
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英文标题：
《Distance-Based Bias in Model-Directed Optimization of Additively
  Decomposable Problems》
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作者：
Martin Pelikan and Mark W. Hauschild
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
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PDF链接：
https://arxiv.org/pdf/1201.2241

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关键词：Optimization Intelligence Presentation distribution interactions 距离 提高 运行 优化 信息